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Improve README, minor changes in procedural example

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Dominik Jain 2024-03-02 13:17:15 +01:00 committed by Michael Panchenko
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44
README.md
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@ -6,10 +6,10 @@
[![PyPI](https://img.shields.io/pypi/v/tianshou)](https://pypi.org/project/tianshou/) [![Conda](https://img.shields.io/conda/vn/conda-forge/tianshou)](https://github.com/conda-forge/tianshou-feedstock) [![Read the Docs](https://img.shields.io/readthedocs/tianshou)](https://tianshou.readthedocs.io/en/master) [![Read the Docs](https://img.shields.io/readthedocs/tianshou-docs-zh-cn?label=%E4%B8%AD%E6%96%87%E6%96%87%E6%A1%A3)](https://tianshou.readthedocs.io/zh/master/) [![Unittest](https://github.com/thu-ml/tianshou/actions/workflows/pytest.yml/badge.svg)](https://github.com/thu-ml/tianshou/actions) [![codecov](https://img.shields.io/codecov/c/gh/thu-ml/tianshou)](https://codecov.io/gh/thu-ml/tianshou) [![GitHub issues](https://img.shields.io/github/issues/thu-ml/tianshou)](https://github.com/thu-ml/tianshou/issues) [![GitHub stars](https://img.shields.io/github/stars/thu-ml/tianshou)](https://github.com/thu-ml/tianshou/stargazers) [![GitHub forks](https://img.shields.io/github/forks/thu-ml/tianshou)](https://github.com/thu-ml/tianshou/network) [![GitHub license](https://img.shields.io/github/license/thu-ml/tianshou)](https://github.com/thu-ml/tianshou/blob/master/LICENSE)
> ⚠️️ **Dropped support of Gym**:
> Tianshou no longer supports `gym`, and we recommend that you transition to
> ⚠️️ **Dropped support for Gym**:
> Tianshou no longer supports Gym, and we recommend that you transition to
> [Gymnasium](http://github.com/Farama-Foundation/Gymnasium).
> If you absolutely have to use gym, you can try using [Shimmy](https://github.com/Farama-Foundation/Shimmy)
> If you absolutely have to use Gym, you can try using [Shimmy](https://github.com/Farama-Foundation/Shimmy)
> (the compatibility layer), but Tianshou provides no guarantees that things will work then.
> ⚠️️ **Current Status**: the Tianshou master branch is currently under heavy development,
@ -179,7 +179,7 @@ Find example scripts in the [test/](https://github.com/thu-ml/tianshou/blob/mast
<sup>(4): super fast APPO!</sup>
### High quality software engineering standard
### High Software Engineering Standards
| RL Platform | Documentation | Code Coverage | Type Hints | Last Update |
| ------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------ | ----------------------------------------------------------------------------------------------------------------- |
@ -233,8 +233,6 @@ We shall apply the deep Q network (DQN) learning algorithm using both APIs.
### High-Level API
The high-level API requires the extra package `argparse` (by adding
`--extras argparse`) to be installed.
To get started, we need some imports.
```python
@ -333,11 +331,15 @@ Here's a run (with the training time cut short):
<img src="docs/_static/images/discrete_dqn_hl.gif">
</p>
Find many further applications of the high-level API in the `examples/` folder;
look for scripts ending with `_hl.py`.
Note that most of these examples require the extra package `argparse`
(install it by adding `--extras argparse` when invoking poetry).
### Procedural API
Let us now consider an analogous example in the procedural API.
Find the full script from which the snippets below were derived at [test/discrete/test_dqn.py](https://github.com/thu-ml/tianshou/blob/master/test/discrete/test_dqn.py).
Find the full script in [examples/discrete/discrete_dqn.py](https://github.com/thu-ml/tianshou/blob/master/examples/discrete/discrete_dqn.py).
First, import some relevant packages:
@ -358,24 +360,30 @@ gamma, n_step, target_freq = 0.9, 3, 320
buffer_size = 20000
eps_train, eps_test = 0.1, 0.05
step_per_epoch, step_per_collect = 10000, 10
logger = ts.utils.TensorboardLogger(SummaryWriter('log/dqn')) # TensorBoard is supported!
# For other loggers: https://tianshou.readthedocs.io/en/master/01_tutorials/05_logger.html
```
Initialize the logger:
```python
logger = ts.utils.TensorboardLogger(SummaryWriter('log/dqn'))
# For other loggers, see https://tianshou.readthedocs.io/en/master/01_tutorials/05_logger.html
```
Make environments:
```python
# you can also try with SubprocVectorEnv
# You can also try SubprocVectorEnv, which will use parallelization
train_envs = ts.env.DummyVectorEnv([lambda: gym.make(task) for _ in range(train_num)])
test_envs = ts.env.DummyVectorEnv([lambda: gym.make(task) for _ in range(test_num)])
```
Define the network:
Create the network as well as its optimizer:
```python
from tianshou.utils.net.common import Net
# you can define other net by following the API:
# https://tianshou.readthedocs.io/en/master/01_tutorials/00_dqn.html#build-the-network
# Note: You can easily define other networks.
# See https://tianshou.readthedocs.io/en/master/01_tutorials/00_dqn.html#build-the-network
env = gym.make(task, render_mode="human")
state_shape = env.observation_space.shape or env.observation_space.n
action_shape = env.action_space.shape or env.action_space.n
@ -383,7 +391,7 @@ net = Net(state_shape=state_shape, action_shape=action_shape, hidden_sizes=[128,
optim = torch.optim.Adam(net.parameters(), lr=lr)
```
Setup policy and collectors:
Set up the policy and collectors:
```python
policy = ts.policy.DQNPolicy(
@ -419,14 +427,14 @@ result = ts.trainer.OffpolicyTrainer(
print(f"Finished training in {result.timing.total_time} seconds")
```
Save / load the trained policy (it's exactly the same as PyTorch `nn.module`):
Save/load the trained policy (it's exactly the same as loading a `torch.nn.module`):
```python
torch.save(policy.state_dict(), 'dqn.pth')
policy.load_state_dict(torch.load('dqn.pth'))
```
Watch the performance with 35 FPS:
Watch the agent with 35 FPS:
```python
policy.eval()
@ -435,13 +443,13 @@ collector = ts.data.Collector(policy, env, exploration_noise=True)
collector.collect(n_episode=1, render=1 / 35)
```
Look at the result saved in tensorboard: (with bash script in your terminal)
Inspect the data saved in TensorBoard:
```bash
$ tensorboard --logdir log/dqn
```
You can check out the [documentation](https://tianshou.readthedocs.io) for advanced usage.
Please read the [documentation](https://tianshou.readthedocs.io) for advanced usage.
## Contributing

20
examples/discrete/discrete_dqn.py
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@ -1,11 +1,8 @@
from typing import cast
import gymnasium as gym
import torch
from torch.utils.tensorboard import SummaryWriter
import tianshou as ts
from tianshou.utils.space_info import SpaceInfo
def main() -> None:
@ -16,22 +13,21 @@ def main() -> None:
buffer_size = 20000
eps_train, eps_test = 0.1, 0.05
step_per_epoch, step_per_collect = 10000, 10
logger = ts.utils.TensorboardLogger(SummaryWriter("log/dqn")) # TensorBoard is supported!
# For other loggers: https://tianshou.readthedocs.io/en/master/tutorials/logger.html
# you can also try with SubprocVectorEnv
logger = ts.utils.TensorboardLogger(SummaryWriter("log/dqn")) # TensorBoard is supported!
# For other loggers, see https://tianshou.readthedocs.io/en/master/tutorials/logger.html
# You can also try SubprocVectorEnv, which will use parallelization
train_envs = ts.env.DummyVectorEnv([lambda: gym.make(task) for _ in range(train_num)])
test_envs = ts.env.DummyVectorEnv([lambda: gym.make(task) for _ in range(test_num)])
from tianshou.utils.net.common import Net
# you can define other net by following the API:
# https://tianshou.readthedocs.io/en/master/tutorials/dqn.html#build-the-network
# Note: You can easily define other networks.
# See https://tianshou.readthedocs.io/en/master/01_tutorials/00_dqn.html#build-the-network
env = gym.make(task, render_mode="human")
env.action_space = cast(gym.spaces.Discrete, env.action_space)
space_info = SpaceInfo.from_env(env)
state_shape = space_info.observation_info.obs_shape
action_shape = space_info.action_info.action_shape
state_shape = env.observation_space.shape or env.observation_space.n
action_shape = env.action_space.shape or env.action_space.n
net = Net(state_shape=state_shape, action_shape=action_shape, hidden_sizes=[128, 128, 128])
optim = torch.optim.Adam(net.parameters(), lr=lr)